Radio frequency fingerprint identification for Internet of Things: A survey
Radio frequency fingerprint (RFF) identification is a promising technique for identifying Internet of Things (IoT) devices. This paper presents a comprehensive survey on RFF identification, which covers various aspects ranging from related definitions to details of each stage in the identification p...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2024-01-01
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| Series: | Security and Safety |
| Subjects: | |
| Online Access: | https://sands.edpsciences.org/articles/sands/full_html/2024/01/sands20230017/sands20230017.html |
| _version_ | 1827372180558577664 |
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| author | Xie Lingnan Peng Linning Zhang Junqing Hu Aiqun |
| author_facet | Xie Lingnan Peng Linning Zhang Junqing Hu Aiqun |
| author_sort | Xie Lingnan |
| collection | DOAJ |
| description | Radio frequency fingerprint (RFF) identification is a promising technique for identifying Internet of Things (IoT) devices. This paper presents a comprehensive survey on RFF identification, which covers various aspects ranging from related definitions to details of each stage in the identification process, namely signal preprocessing, RFF feature extraction, further processing, and RFF identification. Specifically, three main steps of preprocessing are summarized, including carrier frequency offset estimation, noise elimination, and channel cancellation. Besides, three kinds of RFFs are categorized, comprising I/Q signal-based, parameter-based, and transformation-based features. Meanwhile, feature fusion and feature dimension reduction are elaborated as two main further processing methods. Furthermore, a novel framework is established from the perspective of closed set and open set problems, and the related state-of-the-art methodologies are investigated, including approaches based on traditional machine learning, deep learning, and generative models. Additionally, we highlight the challenges faced by RFF identification and point out future research trends in this field. |
| first_indexed | 2024-03-08T10:53:46Z |
| format | Article |
| id | doaj.art-d82f970b79234320bff0ee7bdcb7b6be |
| institution | Directory Open Access Journal |
| issn | 2826-1275 |
| language | English |
| last_indexed | 2024-03-08T10:53:46Z |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | Security and Safety |
| spelling | doaj.art-d82f970b79234320bff0ee7bdcb7b6be2024-01-26T16:42:36ZengEDP SciencesSecurity and Safety2826-12752024-01-013202302210.1051/sands/2023022sands20230017Radio frequency fingerprint identification for Internet of Things: A surveyXie Lingnan0https://orcid.org/0009-0008-3753-1231Peng Linning1https://orcid.org/0000-0001-5859-7119Zhang Junqing2https://orcid.org/0000-0002-3502-2926Hu Aiqun3https://orcid.org/0000-0002-0398-4899School of Cyber Science and Engineering, Southeast UniversitySchool of Cyber Science and Engineering, Southeast UniversityDepartment of Electrical Engineering and Electronics, University of Liverpool L69 3GJPurple Mountain Laboratories for Network and Communication SecurityRadio frequency fingerprint (RFF) identification is a promising technique for identifying Internet of Things (IoT) devices. This paper presents a comprehensive survey on RFF identification, which covers various aspects ranging from related definitions to details of each stage in the identification process, namely signal preprocessing, RFF feature extraction, further processing, and RFF identification. Specifically, three main steps of preprocessing are summarized, including carrier frequency offset estimation, noise elimination, and channel cancellation. Besides, three kinds of RFFs are categorized, comprising I/Q signal-based, parameter-based, and transformation-based features. Meanwhile, feature fusion and feature dimension reduction are elaborated as two main further processing methods. Furthermore, a novel framework is established from the perspective of closed set and open set problems, and the related state-of-the-art methodologies are investigated, including approaches based on traditional machine learning, deep learning, and generative models. Additionally, we highlight the challenges faced by RFF identification and point out future research trends in this field.https://sands.edpsciences.org/articles/sands/full_html/2024/01/sands20230017/sands20230017.htmlradio frequency fingerprint (rff)internet of things (iot)physical layer securityclosed set identificationopen set identificationdeep learning |
| spellingShingle | Xie Lingnan Peng Linning Zhang Junqing Hu Aiqun Radio frequency fingerprint identification for Internet of Things: A survey Security and Safety radio frequency fingerprint (rff) internet of things (iot) physical layer security closed set identification open set identification deep learning |
| title | Radio frequency fingerprint identification for Internet of Things: A survey |
| title_full | Radio frequency fingerprint identification for Internet of Things: A survey |
| title_fullStr | Radio frequency fingerprint identification for Internet of Things: A survey |
| title_full_unstemmed | Radio frequency fingerprint identification for Internet of Things: A survey |
| title_short | Radio frequency fingerprint identification for Internet of Things: A survey |
| title_sort | radio frequency fingerprint identification for internet of things a survey |
| topic | radio frequency fingerprint (rff) internet of things (iot) physical layer security closed set identification open set identification deep learning |
| url | https://sands.edpsciences.org/articles/sands/full_html/2024/01/sands20230017/sands20230017.html |
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